Methods and systems for estimating road surface friction coefficient using self aligning torque

ABSTRACT

Methods and systems for determining road surface information in a vehicle. In one embodiment, the method includes: determining at least one condition assessment value based on steering data; determining a feature set to include at least one of self-aligning torque (SAT), slip angle, SAT variance, steering rate, and lateral acceleration based on the condition assessment value; processing steering data obtained during a steering maneuver and associated with the feature set using a pattern classification technique; and determining a surface type based on the processing.

TECHNICAL FIELD

The technical field generally relates to vehicles, and more particularlyto methods and systems for estimating road surface information for usein controlling a vehicle.

BACKGROUND

It is desirable to know the road surface friction coefficient duringvehicle operation. For example, a control system may use thisinformation to control one or more vehicle components to aid the driverin operating a vehicle in a safe manner. Currently there is no method todirectly measure the road surface friction coefficient. The road surfacefriction coefficient therefore must be estimated using sensorinformation that is available on the vehicle. Conventional techniques ofestimating the road surface friction coefficient may be unreliable asthey are sensitive to different vehicle dynamic behaviors such assteering behaviors, among others.

Accordingly, it is desirable to provide improved methods and systems fordetermining a type of a road surface. Furthermore, other desirablefeatures and characteristics of the present invention will becomeapparent from the subsequent detailed description and the appendedclaims, taken in conjunction with the accompanying drawings and theforegoing technical field and background.

SUMMARY

Methods and systems for determining road surface information in avehicle is provided. In one embodiment, the method includes: determiningat least one condition assessment value based on steering data;determining a feature set to include at least one of self-aligningtorque (SAT), slip angle, SAT variance, steering rate, and lateralacceleration based on the condition assessment value; processingsteering data obtained during a steering maneuver and associated withthe feature set using a pattern classification technique; anddetermining a surface type based on the processing.

In one embodiment, a system includes: a condition assessment module thatdetermines at least one condition assessment value based on steeringdata. A feature set determination module determines a feature set toinclude at least one of self-aligning torque (SAT), slip angle, SATvariance, steering rate, and lateral acceleration based on the conditionassessment value. A surface classification module processes the steeringdata obtained during a steering maneuver and associated with the featureset using a pattern classification technique to determine a surfacetype.

DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a functional block diagram of an exemplary vehicle, inaccordance with various embodiments;

FIG. 2 is a dataflow diagram illustrating a control module of thevehicle, in accordance with various embodiments; and

FIGS. 3 and 4 are flow charts illustrating methods for estimating roadsurface information, in accordance with various embodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It should be understood that throughoutthe drawings, corresponding reference numerals indicate like orcorresponding parts and features. As used herein, the term module refersto any hardware, software, firmware, electronic control component,processing logic, and/or processor device, individually or in anycombination, including without limitation: application specificintegrated circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that executes one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Embodiments of the invention may be described herein in terms offunctional and/or logical block components and various processing steps.It should be appreciated that such block components may be realized byany number of hardware, software, and/or firmware components configuredto perform the specified functions. For example, an embodiment of theinvention may employ various integrated circuit components, e.g., memoryelements, digital signal processing elements, logic elements, look-uptables, or the like, which may carry out a variety of functions underthe control of one or more microprocessors or other control devices. Inaddition, those skilled in the art will appreciate that embodiments ofthe present invention may be practiced in conjunction with any number ofsteering control systems, and that the vehicle system described hereinis merely one example embodiment of the invention.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, and other functionalaspects of the systems (and the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the invention.

With reference to FIG. 1, an exemplary vehicle 100 in part that includesa control system 110 is shown in accordance with exemplary embodiments.As can be appreciated, the vehicle 100 may be any vehicle type thattravels over a road surface. Although the figures shown herein depict anexample with certain arrangements of elements, additional interveningelements, devices, features, or components may be present in actualembodiments. It should also be understood that FIG. 1 is merelyillustrative and may not be drawn to scale.

The control system 110 includes a control module 120 that receivesinputs from one or more sensors 130 of the vehicle 100. The sensors 130sense observable conditions of the vehicle 100 and generate sensorsignals based thereon. For example, the sensors 130 may sense conditionsof an electric power steering system 140 of the vehicle 100, an inertialmeasurement unit 150 of the vehicle 100, and/or other systems of thevehicle 100 and generate sensor signals based thereon. In variousembodiments, the sensors 130 communicate the signals directly to thecontrol module 120 and/or may communicate the signals 130 to othercontrol modules (not shown) which, in turn, communicate data from thesignals to the control module 120 over a communication bus (not shown)or other communication means.

The control module 120 receives the signals and/or the data captured bythe sensors and estimates a surface type and a surface value(correlating with the road surface friction coefficient) based thereon.In various embodiments, as will be discussed in greater detail below,the control module 120 determines the surface type based on amulti-classifier pattern classification technique that evaluates dataobtained during a steering maneuver. The surface can be, for example,ice, packed snow, dry, or other type. The control module 120 determinesthe surface value based on the determined surface type. The surfacevalue can be a nominal value, for example, between 0 and 1 that isassociated with the particular surface type. Typical values are 0.1 forice, 0.35 for snow, and 1.0 for dry. The control module 120 generatessignals to control one or more components of the vehicle 100 based onthe surface value and/or the surface type, and/or provides the surfacevalue and/or the surface type to other control systems (not shown) ofthe vehicle 100 for further processing and control of components of thevehicle 100.

Referring now to FIG. 2 and with continued reference to FIG. 1, adataflow diagram illustrates the control module 120 in accordance withvarious exemplary embodiments. As can be appreciated, various exemplaryembodiments of the control module 120, according to the presentdisclosure, may include any number of sub-modules. In various exemplaryembodiments, the sub-modules shown in FIG. 2 may be combined and/orfurther partitioned to similarly estimate road surface information andto control one or more components of the vehicle 100 (FIG. 1) basedthereon. In various exemplary embodiments, the control module 120includes a pre-processing module 160, a condition assessment module 170,a surface classification module 180, and a decision making module 190.

The pre-processing module 160 receives as input steering data 200 thatis sensed by the sensors 130 (FIG. 1) and/or determined by the controlmodule 120 or other control modules (not shown) over a particular timeperiod. The steering data 200 includes, but is not limited to, steeringangle data, yaw rate data, longitudinal velocity data, pinion angledata, motor torque data, torsion bar torque data, and lateralacceleration data, among other data. The pre-processing modulepre-processes the steering data 200 to remove noise and otherinaccuracies. The pre-processing module 160 then uses the pre-processeddata 280 to determine self-aligning torque (SAT) data, slip angle data,lateral acceleration data, and steering rate data. For example, the SATdata represents torque that a tire creates as it is steered along asurface which tends to align the tire with the vehicle direction oftravel. The pre-processing module 160 determines the SAT data and otherdata using methods commonly known in the art.

The condition assessment module 170 receives as input the pre-processeddata 280 including the steering rate data, the steering angle data, theSAT data, the slip angle data, and the lateral acceleration data. Basedon the inputs, the condition assessment module 170 determines conditionassessment values 310. In various embodiments, the condition assessmentvalues 310 include a steering mode 320, a SAT mode 330, and a steeringmaneuver mode 340.

In various embodiments, the condition assessment module 170 evaluatesthe steering rate data to determine the steering mode 320. The conditionassessment module 170 sets the steering mode 320 to indicate a steeringspeed, such as, fast steering or normal steering. For example, if thevalue of the steering rate data is large (e.g., greater than athreshold), then the condition assessment module 170 sets the steeringmode 320 to indicate fast steering. In another example, if the value ofthe steering rate data is small (e.g., smaller than a threshold), thenthe condition assessment module 170 sets the steering mode 320 toindicate normal steering. As can be appreciated, in various embodiments,the condition assessment module 170 can set the steering mode 320 toindicate other steering speeds and is not limited to the presentexamples.

In various embodiments, the condition assessment module 170 evaluatesthe SAT data, and the lateral acceleration data to determine the SATmode 330. The condition assessment module 170 sets the SAT mode 330 toindicate a linearity of the SAT data such as, linear and non-linear. Forexample, if the magnitudes of the SAT data are increasing and themagnitudes of the lateral acceleration data are increasing, thecondition assessment module 170 sets the SAT mode 330 to indicatelinear. In another example, if the magnitudes of the SAT data aredecreasing and the magnitudes of the lateral acceleration dataincreasing, the condition assessment module 270 sets the SAT mode 330 toindicate non-linear. As can be appreciated, in various embodiments, thecondition assessment module 170 can set the SAT mode 330 to indicateother forms of linearity and is not limited to the present examples.

In various embodiments, the condition assessment module 170 evaluatesthe steering angle data to determine the steering maneuver mode 340. Thecondition assessment module 170 sets the steering maneuver mode 340 toindicate a steering maneuver type such as, a steering out maneuver, or anon-steering out maneuver. For example, the condition assessment module170 tracks the steering angle data based on two (or more) temporalmoving windows of different sizes. If increments exist in both windowsand the increments exceed a threshold, then the condition assessmentmodule 170 determines that the data is associated with a steering outmaneuver and sets the steering maneuver mode 340 to indicate steeringout maneuver. If, however an increment does not exist in both windows oran increment does not exceed a threshold, then the condition assessmentmodule 170 determines that the data is not associated with a steeringout maneuver and sets the steering maneuver mode 340 to indicate anon-steering out maneuver. As can be appreciated, in variousembodiments, the condition assessment module 170 can set the steeringmaneuver mode 340 to indicate other steering maneuvers and is notlimited to the present examples.

The surface classification module 180 receives as input thepre-processed data 280 including the steering rate data, the steeringangle data, the slip angle data, the SAT data, and the lateralacceleration data. In addition, the surface classification module 180receives the condition assessment values 310 including the steering mode320, the SAT mode 330, and the steering maneuver mode 340. Based on theinputs, the surface classification module 180 determines a surface type350 and a surface value 360. For example, the surface classificationmodule 180 first evaluates the condition assessment values 310 alongwith the slip angle data to select a feature set from a number offeature sets. The feature set defines the data to be used in furtherevaluations.

In various embodiments, the feature sets can include, but are notlimited to: set 1 including SAT and slip angle; set 2 including SAT ,SAT variance, and slip angle; set 3 including lateral acceleration,steering rate, and slip angle; set 4 including lateral acceleration; andset 5 a default set. The surface classification module 180 selects thefeature set 1 when the slip angle is greater than a threshold, the SATmode indicates linear, and the steering mode indicates normal steering.The surface classification module 180 selects the feature set 2 when theslip angle is within a range, the SAT mode indicates linear, and thesteering mode indicates normal steering. The surface classificationmodule 180 selects the feature set 3 when the slip angle is greater thana threshold, the SAT mode indicates linear, and the steering modeindicates fast steering. The surface classification module 180 selectsthe feature set 4 when the SAT mode indicates non-linear. The surfaceclassification module 180 selects the feature set 5 when the steeringmaneuver mode indicates a non-steering out maneuver.

The surface classification module 180 then uses the data associated withthe feature set to identify the surface type 350. For example, when thefeature set is set to one of set 1, set 2, and set 3, the surfaceclassification module 180 evaluates the data associated with the featureset based on a statistical pattern classification method. In anotherexample, when the feature set is set to one of set 4, and set 5 astatistical analysis is not performed rather when the feature set is set4, the SAT mode is non-linear, and the surface type 350 and the surfacevalue 360 can be readily determined based on the value of lateralacceleration, and when the features set is set 5 default values are usedfor the surface type 350 and the surface value 360.

In various embodiments, the statistical pattern classification methodcompares the real-time data associated with the feature set withpre-stored data associated with the same feature set representingtypical patterns of the various road surfaces. The pre-stored data maybe stored in a patterns datastore 370. The statistical patternclassification method used for identifying the surface type 350 can be,but is not limited to, a linear discriminant analysis (LDA) (e.g., BatchPerception, Fisher Linear Discriminant, and so on), support vectormachine (SVM), or other classification method.

The surface classification module 180 then determines the surface value360 to be the nominal value associated with the determined surface type350. The nominal values and their associations with the surface type 350may be pre-determined and stored in the patterns data store 370.

The decision making module 190 receives as input the pre-processed data280 including the steering angle data, the lateral acceleration data,and the surface type 350, and the surface value 360. Based on theinputs, the decision making module 190 determines a final surface type380 and a final surface value 390. For example, when the steering angleindicates that the data corresponds to a steering out maneuver, thelateral acceleration data is tracked and evaluated to see if itcorresponds to the surface type 350. If the lateral acceleration islarge (e.g., greater than a threshold) and the surface type 350 is ahigh friction type (e.g., dry surface), then the surface type 350 isconfirmed as valid and the final surface type 380 is determined to be ahigh friction type. If the lateral acceleration is large (e.g., greaterthan a threshold) and the surface type 350, however, is a low frictiontype (e.g., an icy surface), then the surface type 350 being a lowfriction surface is a false detection result, (i.e., since a lowfriction type of surface is unlikely to have a large lateralacceleration). In this case, the decision making module 190 sets the thefinal surface type 380 and the final surface value 390 to ahigh-friction surface type (e.g., dry surface).

In various embodiments, the decision making module 190 determines thefinal surface type 380 based on an analysis of a sequence of singledecision points within a moving temporal window frame. For example, thedecision making module 190 determines the final surface type 380 bytracking the determined surface value 360 over a window of time. Invarious embodiments, the window of time can have different sizes and canbe reset at different instances.

With reference now to FIGS. 3 and 4, and with continued reference toFIGS. 1-2, flowcharts are shown of methods 400 and 600 for determining asurface type and surface value and controlling a vehicle based on thesurface type and the surface value, in accordance with variousembodiments. The methods 400 and 600 can be implemented in connectionwith the vehicle 100 of FIG. 1 and can be performed by the controlmodule 120 of FIG. 1, in accordance with various exemplary embodiments.As can be appreciated in light of the disclosure, the order of operationwithin the method is not limited to the sequential execution asillustrated in FIGS. 3 and 4, but may be performed in one or morevarying orders as applicable and in accordance with the presentdisclosure. As can further be appreciated, the methods of FIGS. 3 and 4may be scheduled to run at predetermined time intervals during operationof the vehicle 100 and/or may be scheduled to run based on predeterminedevents.

FIG. 3 is a flowchart of a method for determining the final surface type380 and the final surface value 390 and for controlling the vehicle 100based thereon. As depicted in FIG. 3, the method 400 may begin at 405.The steering data 200 is collected and pre-processed at 410. The SATdata and the slip angle data are determined based on the collectedsteering data 200 at 420. Thereafter, the condition assessment values310 are determined based on the pre-processed data 280 at 430. Inparticular, the steering maneuver mode 340 is determined based on thesteering angle at 440. The steering mode 320 is determined based on thesteering rate at 450, and the SAT mode 330 is determined based on theSAT data and lateral acceleration data at 460.

Thereafter, the feature set is determined based on the conditionassessment values 310 at 470. The data associated with the feature setis processed using a classification method (e.g., linear discriminantanalysis or other methods) and the stored patterns to identify thesurface type 350 at 480. The surface value 360 is determined based onthe surface type 350 at 490. The surface type 350 is confirmed based onan evaluation of the lateral acceleration at 500. If the surface type350 is confirmed at 510, the surface value 360 and the surface type 350are processed to determine the final surface type 380 and the finalsurface value 390 at 520. Thereafter, one or more systems of the vehicleare controlled based on the final surface type 380 and/or the finalsurface value 390 at 530 and the method may end at 540. If, however, thesurface type 350 is not confirmed at 530, the method may end at 540.

FIG. 4 is a flowchart of a method 600 for determining the feature set asshown at 470 of FIG. 3. The method may begin at 605. The steeringmaneuver mode 340 is evaluated at 610. If the steering maneuver mode 340does not indicate a steering out maneuver at 610, the method proceeds to620, where the surface type 350 and the surface value 360 are set todefault values (e.g., a previous value, or other predetermined defaultvalue) and the method may end at 630.

If, however, the steering maneuver mode 340 indicates a steering outmaneuver at 610, the SAT mode 330 is evaluated at 640. If the SAT mode330 indicates a non-linear SAT, set 4 is selected as the feature set,which includes the lateral acceleration at 650. Thereafter, the methodmay end at 630.

If, however, the SAT mode 330 indicates a linear SAT at 640, thesteering mode 320 is evaluated at 660. If the steering mode 320indicates fast steering at 660, the slip angle data is evaluated at 670.If the slip angle is less than a threshold at 670, the method proceedsto 620, where the surface type 350 and the surface value 360 are set todefault values (e.g., a previous value, or other predetermined defaultvalue) and the method may end at 630.

If, however, at 670, the slip angle is greater than the threshold, set 3is selected as the feature set at 675, which includes the lateralacceleration, the steering rate, and the slip angle. Thereafter, themethod may end at 630.

If, at 660, the steering mode indicates does not indicate fast steeringrather indicates normal steering, the slip angle data is evaluated at680. For example, if the slip angle is greater than a first threshold(e.g., a high threshold indicating a large slip) at 680, set 1 isselected as the feature set, which includes the SAT, and the slip angleat 690. Thereafter, the method may end at 630.

If, however, the slip angle is less than the first threshold at 680, theslip angle is compared with a second threshold (e.g., a low thresholdindicating a small slip) at 700. If the slip angle is greater than thesecond threshold at 700, set 2 is selected as the feature set, whichincludes the SAT, the SAT variance, and the slip angle at 710.Thereafter, the method may end at 630. If, however, the slip angle isless than the second threshold at 700, the method proceeds to 620 thesurface type 350 and the surface value 360 are set to default values(e.g., a previous value, or other predetermined default value) and themethod may end at 630.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Itshould be understood that various changes can be made in the functionand arrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

What is claimed is:
 1. A method for determining road surface informationin a vehicle, comprising: determining at least one condition assessmentvalue based on steering data; determining a feature set to include atleast one of self-aligning torque (SAT), slip angle, SAT variance,steering rate, and lateral acceleration based on the conditionassessment value; processing steering data obtained during a steeringmaneuver and associated with the feature set using a patternclassification technique; and determining a surface type based on theprocessing.
 2. The method of claim 1, wherein the at least one conditionassessment value is a steering mode that is associated with a steeringrate.
 3. The method of claim 1, wherein the at least one conditionassessment value is a SAT mode that is associated with a linearity ofthe SAT.
 4. The method of claim 1, further comprising determining asteering maneuver type, and wherein the determining the feature set andthe processing the steering data is based on the steering maneuver type.5. The method of claim 4, wherein the steering maneuver type is at leastone of a steering out maneuver and a non-steering out maneuver.
 6. Themethod of claim 5, further comprising determining a steering maneuvertype, and wherein the determining the surface type is based on a defaultvalue when the steering maneuver type is determined to be thenon-steering out maneuver.
 7. The method of claim 1, wherein thedetermining the feature set comprises determining the feature set toinclude SAT and slip angle.
 8. The method of claim 7, furthercomprising: determining a slip angle to be greater than a threshold, andwherein the determining the condition assessment value comprisesdetermining a SAT mode to be linear and determining the steering mode tobe normal steering, and wherein the determining the feature set toinclude SAT and slip angle is based on the slip angle being greater thana threshold, the SAT mode being linear, and the steering mode beingnormal steering.
 9. The method of claim 1, wherein the determining thefeature set comprises determining the feature set to include SAT, SATvariance, and slip angle.
 10. The method of claim 9, further comprising:determining a slip angle to be within a range, and wherein thedetermining the condition assessment value comprises determining a SATmode to be linear and determining the steering mode to be normalsteering, and wherein the determining the feature set to include SAT,SAT variance, and slip angle is based on the slip angle being within therange, the SAT mode being linear, and the steering mode being normalsteering.
 11. The method of claim 1, wherein the determining the featureset comprises determining the feature set to include lateralacceleration, steering rate, and slip angle.
 12. The method of claim 11,further comprising: determining a slip angle to be greater than athreshold, and wherein the determining the condition assessment valuecomprises determining a SAT mode to be linear and determining thesteering mode to be fast steering, and wherein the determining thefeature set to include lateral acceleration, steering rate, and slipangle is based on the slip angle being greater than the threshold, theSAT mode being linear, and the steering mode being fast steering. 13.The method of claim 1, wherein the determining the feature set comprisesdetermining the feature set to include lateral acceleration.
 14. Themethod of claim 13, further comprising: wherein the determining thecondition assessment value comprises determining a SAT mode to benonlinear, and wherein the determining the feature set to includelateral acceleration is based on the SAT mode being nonlinear.
 15. Themethod of claim 1, further comprising determining a surface value basedon the surface type.
 16. The method of claim 1, wherein the patternclassification technique includes at least one of a linear discriminantanalysis and a support vector machine analysis.
 17. The method of claim1, further comprising confirming the surface type based on an evaluationof lateral acceleration.
 18. The method of claim 1, further comprisingdetermining a final surface type based on a plurality of determinedsurface types within a temporal moving window.
 19. A system fordetermining road surface information in a vehicle, comprising: acondition assessment module that determines at least one conditionassessment value based on steering data; a feature set determinationmodule that determines a feature set to include at least one ofself-aligning torque (SAT), slip angle, SAT variance, steering rate, andlateral acceleration based on the condition assessment value; and asurface classification module that processes the steering data obtainedduring a steering maneuver and associated with the feature set using apattern classification technique to determine a surface type.
 20. Thesystem of claim 19 further comprising a decision making module thatdetermines a final surface type based on a plurality of determinedsurface types within a temporal moving window.